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street-fighter-ai/training_history.txt
linyiLYi 4a35b81937 add cv method
2023-03-28 01:31:23 +08:00

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(StreetFighterAI) PS C:\Users\unitec\Documents\AIProjects\street-fighter-ai> python .\train_street_fighter_ai.py
Importing StreetFighterIISpecialChampionEdition-Genesis
Imported 1 games
Using cuda device
Wrapping the env in a VecTransposeImage.
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| time/ | |
| fps | 990 |
| iterations | 1 |
| time_elapsed | 21 |
| total_timesteps | 21600 |
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| time/ | |
| fps | 339 |
| iterations | 2 |
| time_elapsed | 127 |
| total_timesteps | 43200 |
| train/ | |
| approx_kl | 56.143074 |
| clip_fraction | 0.975 |
| clip_range | 0.2 |
| entropy_loss | -1.29 |
| explained_variance | 1.98e-05 |
| learning_rate | 0.00025 |
| loss | 771 |
| n_updates | 10 |
| policy_gradient_loss | 0.302 |
| value_loss | 1.81e+05 |
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| time/ | |
| fps | 284 |
| iterations | 3 |
| time_elapsed | 227 |
| total_timesteps | 64800 |
| train/ | |
| approx_kl | 0.6508633 |
| clip_fraction | 0.195 |
| clip_range | 0.2 |
| entropy_loss | -0.256 |
| explained_variance | -0.059 |
| learning_rate | 0.00025 |
| loss | 26.3 |
| n_updates | 20 |
| policy_gradient_loss | 0.00946 |
| value_loss | 465 |
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| time/ | |
| fps | 262 |
| iterations | 4 |
| time_elapsed | 328 |
| total_timesteps | 86400 |
| train/ | |
| approx_kl | 0.48297837 |
| clip_fraction | 0.343 |
| clip_range | 0.2 |
| entropy_loss | -1.01 |
| explained_variance | 0.523 |
| learning_rate | 0.00025 |
| loss | 2.51 |
| n_updates | 30 |
| policy_gradient_loss | -0.0343 |
| value_loss | 5.95 |
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| time/ | |
| fps | 251 |
| iterations | 5 |
| time_elapsed | 428 |
| total_timesteps | 108000 |
| train/ | |
| approx_kl | 7.8073545 |
| clip_fraction | 0.45 |
| clip_range | 0.2 |
| entropy_loss | -0.067 |
| explained_variance | 0.0212 |
| learning_rate | 0.00025 |
| loss | 47.4 |
| n_updates | 40 |
| policy_gradient_loss | 0.129 |
| value_loss | 810 |
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| time/ | |
| fps | 244 |
| iterations | 6 |
| time_elapsed | 529 |
| total_timesteps | 129600 |
| train/ | |
| approx_kl | 0.21164177 |
| clip_fraction | 0.156 |
| clip_range | 0.2 |
| entropy_loss | -0.398 |
| explained_variance | -0.251 |
| learning_rate | 0.00025 |
| loss | 43.3 |
| n_updates | 50 |
| policy_gradient_loss | -0.00713 |
| value_loss | 87.6 |
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| time/ | |
| fps | 240 |
| iterations | 7 |
| time_elapsed | 628 |
| total_timesteps | 151200 |
| train/ | |
| approx_kl | 0.52084094 |
| clip_fraction | 0.374 |
| clip_range | 0.2 |
| entropy_loss | -0.571 |
| explained_variance | 0.135 |
| learning_rate | 0.00025 |
| loss | 5.23 |
| n_updates | 60 |
| policy_gradient_loss | 0.0252 |
| value_loss | 401 |
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| time/ | |
| fps | 237 |
| iterations | 8 |
| time_elapsed | 728 |
| total_timesteps | 172800 |
| train/ | |
| approx_kl | 0.79960424 |
| clip_fraction | 0.342 |
| clip_range | 0.2 |
| entropy_loss | -0.483 |
| explained_variance | 0.231 |
| learning_rate | 0.00025 |
| loss | 50.3 |
| n_updates | 70 |
| policy_gradient_loss | 0.0144 |
| value_loss | 770 |
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| time/ | |
| fps | 234 |
| iterations | 9 |
| time_elapsed | 827 |
| total_timesteps | 194400 |
| train/ | |
| approx_kl | 0.16273381 |
| clip_fraction | 0.409 |
| clip_range | 0.2 |
| entropy_loss | -0.701 |
| explained_variance | 0.3 |
| learning_rate | 0.00025 |
| loss | 891 |
| n_updates | 80 |
| policy_gradient_loss | 0.00848 |
| value_loss | 459 |
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| time/ | |
| fps | 232 |
| iterations | 10 |
| time_elapsed | 928 |
| total_timesteps | 216000 |
| train/ | |
| approx_kl | 0.26048473 |
| clip_fraction | 0.366 |
| clip_range | 0.2 |
| entropy_loss | -0.829 |
| explained_variance | 0.675 |
| learning_rate | 0.00025 |
| loss | 7.25 |
| n_updates | 90 |
| policy_gradient_loss | -0.00101 |
| value_loss | 32.3 |
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| time/ | |
| fps | 230 |
| iterations | 11 |
| time_elapsed | 1028 |
| total_timesteps | 237600 |
| train/ | |
| approx_kl | 0.124250144 |
| clip_fraction | 0.362 |
| clip_range | 0.2 |
| entropy_loss | -1.05 |
| explained_variance | 0.801 |
| learning_rate | 0.00025 |
| loss | 3.48 |
| n_updates | 100 |
| policy_gradient_loss | 0.0428 |
| value_loss | 14 |
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| time/ | |
| fps | 229 |
| iterations | 12 |
| time_elapsed | 1128 |
| total_timesteps | 259200 |
| train/ | |
| approx_kl | 0.6506246 |
| clip_fraction | 0.387 |
| clip_range | 0.2 |
| entropy_loss | -1.02 |
| explained_variance | 0.82 |
| learning_rate | 0.00025 |
| loss | 1.37 |
| n_updates | 110 |
| policy_gradient_loss | -0.0139 |
| value_loss | 8.15 |
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| time/ | |
| fps | 231 |
| iterations | 13 |
| time_elapsed | 1212 |
| total_timesteps | 280800 |
| train/ | |
| approx_kl | 2.5178356 |
| clip_fraction | 0.418 |
| clip_range | 0.2 |
| entropy_loss | -1.07 |
| explained_variance | 0.153 |
| learning_rate | 0.00025 |
| loss | 2.92 |
| n_updates | 120 |
| policy_gradient_loss | 0.0904 |
| value_loss | 387 |
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| time/ | |
| fps | 230 |
| iterations | 14 |
| time_elapsed | 1312 |
| total_timesteps | 302400 |
| train/ | |
| approx_kl | 1.4066175 |
| clip_fraction | 0.206 |
| clip_range | 0.2 |
| entropy_loss | -0.592 |
| explained_variance | 0.599 |
| learning_rate | 0.00025 |
| loss | 1.15e+03 |
| n_updates | 130 |
| policy_gradient_loss | 0.062 |
| value_loss | 4.33e+03 |
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| time/ | |
| fps | 229 |
| iterations | 15 |
| time_elapsed | 1412 |
| total_timesteps | 324000 |
| train/ | |
| approx_kl | 0.7943301 |
| clip_fraction | 0.382 |
| clip_range | 0.2 |
| entropy_loss | -0.724 |
| explained_variance | 0.499 |
| learning_rate | 0.00025 |
| loss | 5.47 |
| n_updates | 140 |
| policy_gradient_loss | 0.0461 |
| value_loss | 99.8 |
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| time/ | |
| fps | 228 |
| iterations | 16 |
| time_elapsed | 1512 |
| total_timesteps | 345600 |
| train/ | |
| approx_kl | 1.1466624 |
| clip_fraction | 0.162 |
| clip_range | 0.2 |
| entropy_loss | -0.534 |
| explained_variance | 0.508 |
| learning_rate | 0.00025 |
| loss | 43.9 |
| n_updates | 150 |
| policy_gradient_loss | 0.0443 |
| value_loss | 330 |
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| time/ | |
| fps | 227 |
| iterations | 17 |
| time_elapsed | 1612 |
| total_timesteps | 367200 |
| train/ | |
| approx_kl | 0.3199593 |
| clip_fraction | 0.0612 |
| clip_range | 0.2 |
| entropy_loss | -0.236 |
| explained_variance | 0.639 |
| learning_rate | 0.00025 |
| loss | 9.64 |
| n_updates | 160 |
| policy_gradient_loss | 0.0129 |
| value_loss | 217 |
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| time/ | |
| fps | 227 |
| iterations | 18 |
| time_elapsed | 1712 |
| total_timesteps | 388800 |
| train/ | |
| approx_kl | 0.38865572 |
| clip_fraction | 0.0371 |
| clip_range | 0.2 |
| entropy_loss | -0.332 |
| explained_variance | 0.494 |
| learning_rate | 0.00025 |
| loss | 12.6 |
| n_updates | 170 |
| policy_gradient_loss | 0.00872 |
| value_loss | 134 |
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| time/ | |
| fps | 226 |
| iterations | 19 |
| time_elapsed | 1812 |
| total_timesteps | 410400 |
| train/ | |
| approx_kl | 0.8817278 |
| clip_fraction | 0.0944 |
| clip_range | 0.2 |
| entropy_loss | -0.219 |
| explained_variance | 0.727 |
| learning_rate | 0.00025 |
| loss | 4.68 |
| n_updates | 180 |
| policy_gradient_loss | 0.00855 |
| value_loss | 29.2 |
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| time/ | |
| fps | 225 |
| iterations | 20 |
| time_elapsed | 1914 |
| total_timesteps | 432000 |
| train/ | |
| approx_kl | 3.7763007 |
| clip_fraction | 0.25 |
| clip_range | 0.2 |
| entropy_loss | -0.327 |
| explained_variance | 0.484 |
| learning_rate | 0.00025 |
| loss | 25.8 |
| n_updates | 190 |
| policy_gradient_loss | 0.0522 |
| value_loss | 162 |
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| time/ | |
| fps | 225 |
| iterations | 21 |
| time_elapsed | 2014 |
| total_timesteps | 453600 |
| train/ | |
| approx_kl | 1.8167689 |
| clip_fraction | 0.146 |
| clip_range | 0.2 |
| entropy_loss | -0.371 |
| explained_variance | 0.699 |
| learning_rate | 0.00025 |
| loss | 7.1 |
| n_updates | 200 |
| policy_gradient_loss | 0.0449 |
| value_loss | 68.5 |
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| time/ | |
| fps | 224 |
| iterations | 22 |
| time_elapsed | 2113 |
| total_timesteps | 475200 |
| train/ | |
| approx_kl | 1.1459472 |
| clip_fraction | 0.196 |
| clip_range | 0.2 |
| entropy_loss | -0.514 |
| explained_variance | 0.549 |
| learning_rate | 0.00025 |
| loss | 4.31 |
| n_updates | 210 |
| policy_gradient_loss | 0.0242 |
| value_loss | 112 |
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| time/ | |
| fps | 224 |
| iterations | 23 |
| time_elapsed | 2213 |
| total_timesteps | 496800 |
| train/ | |
| approx_kl | 7.641809 |
| clip_fraction | 0.326 |
| clip_range | 0.2 |
| entropy_loss | -0.578 |
| explained_variance | 0.527 |
| learning_rate | 0.00025 |
| loss | 813 |
| n_updates | 220 |
| policy_gradient_loss | 0.0566 |
| value_loss | 235 |
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| time/ | |
| fps | 223 |
| iterations | 24 |
| time_elapsed | 2314 |
| total_timesteps | 518400 |
| train/ | |
| approx_kl | 4.9070067 |
| clip_fraction | 0.351 |
| clip_range | 0.2 |
| entropy_loss | -0.692 |
| explained_variance | 0.309 |
| learning_rate | 0.00025 |
| loss | 41.2 |
| n_updates | 230 |
| policy_gradient_loss | 0.067 |
| value_loss | 146 |
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| time/ | |
| fps | 223 |
| iterations | 25 |
| time_elapsed | 2413 |
| total_timesteps | 540000 |
| train/ | |
| approx_kl | 20.996988 |
| clip_fraction | 0.392 |
| clip_range | 0.2 |
| entropy_loss | -0.866 |
| explained_variance | 0.292 |
| learning_rate | 0.00025 |
| loss | 80.3 |
| n_updates | 240 |
| policy_gradient_loss | 0.105 |
| value_loss | 674 |
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| time/ | |
| fps | 223 |
| iterations | 26 |
| time_elapsed | 2512 |
| total_timesteps | 561600 |
| train/ | |
| approx_kl | 13.639556 |
| clip_fraction | 0.322 |
| clip_range | 0.2 |
| entropy_loss | -0.783 |
| explained_variance | 0.458 |
| learning_rate | 0.00025 |
| loss | 95.7 |
| n_updates | 250 |
| policy_gradient_loss | 0.103 |
| value_loss | 3.24e+03 |
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| time/ | |
| fps | 223 |
| iterations | 27 |
| time_elapsed | 2611 |
| total_timesteps | 583200 |
| train/ | |
| approx_kl | 3.7484746 |
| clip_fraction | 0.165 |
| clip_range | 0.2 |
| entropy_loss | -0.388 |
| explained_variance | 0.487 |
| learning_rate | 0.00025 |
| loss | 19.5 |
| n_updates | 260 |
| policy_gradient_loss | 0.0665 |
| value_loss | 267 |
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| time/ | |
| fps | 223 |
| iterations | 28 |
| time_elapsed | 2711 |
| total_timesteps | 604800 |
| train/ | |
| approx_kl | 4.639748 |
| clip_fraction | 0.284 |
| clip_range | 0.2 |
| entropy_loss | -0.65 |
| explained_variance | 0.513 |
| learning_rate | 0.00025 |
| loss | 15.4 |
| n_updates | 270 |
| policy_gradient_loss | 0.0702 |
| value_loss | 251 |
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| time/ | |
| fps | 222 |
| iterations | 29 |
| time_elapsed | 2812 |
| total_timesteps | 626400 |
| train/ | |
| approx_kl | 6.0257225 |
| clip_fraction | 0.3 |
| clip_range | 0.2 |
| entropy_loss | -0.582 |
| explained_variance | 0.719 |
| learning_rate | 0.00025 |
| loss | 16.6 |
| n_updates | 280 |
| policy_gradient_loss | 0.0874 |
| value_loss | 103 |
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| time/ | |
| fps | 222 |
| iterations | 30 |
| time_elapsed | 2913 |
| total_timesteps | 648000 |
| train/ | |
| approx_kl | 3.7680728 |
| clip_fraction | 0.277 |
| clip_range | 0.2 |
| entropy_loss | -0.581 |
| explained_variance | 0.702 |
| learning_rate | 0.00025 |
| loss | 11.9 |
| n_updates | 290 |
| policy_gradient_loss | 0.0532 |
| value_loss | 203 |
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| time/ | |
| fps | 222 |
| iterations | 31 |
| time_elapsed | 3014 |
| total_timesteps | 669600 |
| train/ | |
| approx_kl | 3.082776 |
| clip_fraction | 0.316 |
| clip_range | 0.2 |
| entropy_loss | -0.476 |
| explained_variance | 0.786 |
| learning_rate | 0.00025 |
| loss | 9.55 |
| n_updates | 300 |
| policy_gradient_loss | 0.103 |
| value_loss | 84 |
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| time/ | |
| fps | 221 |
| iterations | 32 |
| time_elapsed | 3115 |
| total_timesteps | 691200 |
| train/ | |
| approx_kl | 3.4251199 |
| clip_fraction | 0.279 |
| clip_range | 0.2 |
| entropy_loss | -0.506 |
| explained_variance | 0.508 |
| learning_rate | 0.00025 |
| loss | 12.9 |
| n_updates | 310 |
| policy_gradient_loss | 0.0868 |
| value_loss | 146 |
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| time/ | |
| fps | 221 |
| iterations | 33 |
| time_elapsed | 3215 |
| total_timesteps | 712800 |
| train/ | |
| approx_kl | 6.858263 |
| clip_fraction | 0.313 |
| clip_range | 0.2 |
| entropy_loss | -0.663 |
| explained_variance | 0.363 |
| learning_rate | 0.00025 |
| loss | 14.2 |
| n_updates | 320 |
| policy_gradient_loss | 0.0548 |
| value_loss | 819 |
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| time/ | |
| fps | 221 |
| iterations | 34 |
| time_elapsed | 3321 |
| total_timesteps | 734400 |
| train/ | |
| approx_kl | 6.3766594 |
| clip_fraction | 0.309 |
| clip_range | 0.2 |
| entropy_loss | -0.61 |
| explained_variance | 0.583 |
| learning_rate | 0.00025 |
| loss | 20.7 |
| n_updates | 330 |
| policy_gradient_loss | 0.145 |
| value_loss | 128 |
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| time/ | |
| fps | 220 |
| iterations | 35 |
| time_elapsed | 3422 |
| total_timesteps | 756000 |
| train/ | |
| approx_kl | 8.304734 |
| clip_fraction | 0.297 |
| clip_range | 0.2 |
| entropy_loss | -0.481 |
| explained_variance | 0.744 |
| learning_rate | 0.00025 |
| loss | 5.62 |
| n_updates | 340 |
| policy_gradient_loss | 0.0571 |
| value_loss | 137 |
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| time/ | |
| fps | 220 |
| iterations | 36 |
| time_elapsed | 3522 |
| total_timesteps | 777600 |
| train/ | |
| approx_kl | 8.265856 |
| clip_fraction | 0.332 |
| clip_range | 0.2 |
| entropy_loss | -0.568 |
| explained_variance | 0.765 |
| learning_rate | 0.00025 |
| loss | 104 |
| n_updates | 350 |
| policy_gradient_loss | 0.0557 |
| value_loss | 868 |
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| time/ | |
| fps | 220 |
| iterations | 37 |
| time_elapsed | 3622 |
| total_timesteps | 799200 |
| train/ | |
| approx_kl | 4.0512986 |
| clip_fraction | 0.238 |
| clip_range | 0.2 |
| entropy_loss | -0.54 |
| explained_variance | 0.742 |
| learning_rate | 0.00025 |
| loss | 19 |
| n_updates | 360 |
| policy_gradient_loss | 0.0648 |
| value_loss | 152 |
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| time/ | |
| fps | 220 |
| iterations | 38 |
| time_elapsed | 3724 |
| total_timesteps | 820800 |
| train/ | |
| approx_kl | 4.704707 |
| clip_fraction | 0.296 |
| clip_range | 0.2 |
| entropy_loss | -0.446 |
| explained_variance | 0.826 |
| learning_rate | 0.00025 |
| loss | 16.2 |
| n_updates | 370 |
| policy_gradient_loss | 0.0675 |
| value_loss | 122 |
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| time/ | |
| fps | 220 |
| iterations | 39 |
| time_elapsed | 3825 |
| total_timesteps | 842400 |
| train/ | |
| approx_kl | 6.0659266 |
| clip_fraction | 0.322 |
| clip_range | 0.2 |
| entropy_loss | -0.575 |
| explained_variance | 0.825 |
| learning_rate | 0.00025 |
| loss | 7.31 |
| n_updates | 380 |
| policy_gradient_loss | 0.0479 |
| value_loss | 66.3 |
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| time/ | |
| fps | 220 |
| iterations | 40 |
| time_elapsed | 3925 |
| total_timesteps | 864000 |
| train/ | |
| approx_kl | 12.445694 |
| clip_fraction | 0.446 |
| clip_range | 0.2 |
| entropy_loss | -0.377 |
| explained_variance | 0.541 |
| learning_rate | 0.00025 |
| loss | 18.8 |
| n_updates | 390 |
| policy_gradient_loss | 0.0929 |
| value_loss | 465 |
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| time/ | |
| fps | 219 |
| iterations | 41 |
| time_elapsed | 4026 |
| total_timesteps | 885600 |
| train/ | |
| approx_kl | 4.830075 |
| clip_fraction | 0.367 |
| clip_range | 0.2 |
| entropy_loss | -0.545 |
| explained_variance | 0.791 |
| learning_rate | 0.00025 |
| loss | 14.7 |
| n_updates | 400 |
| policy_gradient_loss | 0.0392 |
| value_loss | 45.2 |
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| time/ | |
| fps | 219 |
| iterations | 42 |
| time_elapsed | 4126 |
| total_timesteps | 907200 |
| train/ | |
| approx_kl | 5.4566507 |
| clip_fraction | 0.37 |
| clip_range | 0.2 |
| entropy_loss | -0.511 |
| explained_variance | 0.849 |
| learning_rate | 0.00025 |
| loss | 2.3 |
| n_updates | 410 |
| policy_gradient_loss | 0.0485 |
| value_loss | 26.7 |
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| time/ | |
| fps | 219 |
| iterations | 43 |
| time_elapsed | 4226 |
| total_timesteps | 928800 |
| train/ | |
| approx_kl | 24.042978 |
| clip_fraction | 0.591 |
| clip_range | 0.2 |
| entropy_loss | -0.584 |
| explained_variance | 0.369 |
| learning_rate | 0.00025 |
| loss | 13.2 |
| n_updates | 420 |
| policy_gradient_loss | 0.138 |
| value_loss | 342 |
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| time/ | |
| fps | 219 |
| iterations | 44 |
| time_elapsed | 4327 |
| total_timesteps | 950400 |
| train/ | |
| approx_kl | 4.391761 |
| clip_fraction | 0.272 |
| clip_range | 0.2 |
| entropy_loss | -0.305 |
| explained_variance | 0.616 |
| learning_rate | 0.00025 |
| loss | 10.5 |
| n_updates | 430 |
| policy_gradient_loss | 0.0732 |
| value_loss | 215 |
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| time/ | |
| fps | 219 |
| iterations | 45 |
| time_elapsed | 4428 |
| total_timesteps | 972000 |
| train/ | |
| approx_kl | 8.628279 |
| clip_fraction | 0.375 |
| clip_range | 0.2 |
| entropy_loss | -0.571 |
| explained_variance | 0.679 |
| learning_rate | 0.00025 |
| loss | 9.41 |
| n_updates | 440 |
| policy_gradient_loss | 0.0514 |
| value_loss | 164 |
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| time/ | |
| fps | 219 |
| iterations | 46 |
| time_elapsed | 4527 |
| total_timesteps | 993600 |
| train/ | |
| approx_kl | 6.843931 |
| clip_fraction | 0.35 |
| clip_range | 0.2 |
| entropy_loss | -0.484 |
| explained_variance | 0.686 |
| learning_rate | 0.00025 |
| loss | 10.1 |
| n_updates | 450 |
| policy_gradient_loss | 0.0829 |
| value_loss | 143 |
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| time/ | |
| fps | 219 |
| iterations | 47 |
| time_elapsed | 4626 |
| total_timesteps | 1015200 |
| train/ | |
| approx_kl | 8.118596 |
| clip_fraction | 0.416 |
| clip_range | 0.2 |
| entropy_loss | -0.567 |
| explained_variance | 0.503 |
| learning_rate | 0.00025 |
| loss | 15.3 |
| n_updates | 460 |
| policy_gradient_loss | 0.0915 |
| value_loss | 223 |
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| time/ | |
| fps | 219 |
| iterations | 48 |
| time_elapsed | 4726 |
| total_timesteps | 1036800 |
| train/ | |
| approx_kl | 8.13674 |
| clip_fraction | 0.418 |
| clip_range | 0.2 |
| entropy_loss | -0.56 |
| explained_variance | 0.562 |
| learning_rate | 0.00025 |
| loss | 26.2 |
| n_updates | 470 |
| policy_gradient_loss | 0.105 |
| value_loss | 279 |
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| time/ | |
| fps | 219 |
| iterations | 49 |
| time_elapsed | 4827 |
| total_timesteps | 1058400 |
| train/ | |
| approx_kl | 4.1058106 |
| clip_fraction | 0.274 |
| clip_range | 0.2 |
| entropy_loss | -0.296 |
| explained_variance | 0.752 |
| learning_rate | 0.00025 |
| loss | 10.5 |
| n_updates | 480 |
| policy_gradient_loss | 0.0563 |
| value_loss | 103 |
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| time/ | |
| fps | 219 |
| iterations | 50 |
| time_elapsed | 4927 |
| total_timesteps | 1080000 |
| train/ | |
| approx_kl | 15.120241 |
| clip_fraction | 0.459 |
| clip_range | 0.2 |
| entropy_loss | -0.567 |
| explained_variance | 0.423 |
| learning_rate | 0.00025 |
| loss | 30.3 |
| n_updates | 490 |
| policy_gradient_loss | 0.0974 |
| value_loss | 320 |
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| time/ | |
| fps | 219 |
| iterations | 51 |
| time_elapsed | 5028 |
| total_timesteps | 1101600 |
| train/ | |
| approx_kl | 7.0906005 |
| clip_fraction | 0.375 |
| clip_range | 0.2 |
| entropy_loss | -0.456 |
| explained_variance | 0.564 |
| learning_rate | 0.00025 |
| loss | 25.2 |
| n_updates | 500 |
| policy_gradient_loss | 0.0861 |
| value_loss | 324 |
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| time/ | |
| fps | 218 |
| iterations | 52 |
| time_elapsed | 5128 |
| total_timesteps | 1123200 |
| train/ | |
| approx_kl | 6.208802 |
| clip_fraction | 0.353 |
| clip_range | 0.2 |
| entropy_loss | -0.531 |
| explained_variance | 0.622 |
| learning_rate | 0.00025 |
| loss | 15.1 |
| n_updates | 510 |
| policy_gradient_loss | 0.0648 |
| value_loss | 177 |
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| time/ | |
| fps | 218 |
| iterations | 53 |
| time_elapsed | 5228 |
| total_timesteps | 1144800 |
| train/ | |
| approx_kl | 7.811362 |
| clip_fraction | 0.432 |
| clip_range | 0.2 |
| entropy_loss | -0.601 |
| explained_variance | 0.666 |
| learning_rate | 0.00025 |
| loss | 29.4 |
| n_updates | 520 |
| policy_gradient_loss | 0.0799 |
| value_loss | 219 |
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| time/ | |
| fps | 218 |
| iterations | 54 |
| time_elapsed | 5327 |
| total_timesteps | 1166400 |
| train/ | |
| approx_kl | 7.52061 |
| clip_fraction | 0.405 |
| clip_range | 0.2 |
| entropy_loss | -0.52 |
| explained_variance | 0.677 |
| learning_rate | 0.00025 |
| loss | 10.3 |
| n_updates | 530 |
| policy_gradient_loss | 0.0836 |
| value_loss | 179 |
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| time/ | |
| fps | 218 |
| iterations | 55 |
| time_elapsed | 5427 |
| total_timesteps | 1188000 |
| train/ | |
| approx_kl | 4.918111 |
| clip_fraction | 0.402 |
| clip_range | 0.2 |
| entropy_loss | -0.579 |
| explained_variance | 0.805 |
| learning_rate | 0.00025 |
| loss | 14.4 |
| n_updates | 540 |
| policy_gradient_loss | 0.0698 |
| value_loss | 184 |
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| time/ | |
| fps | 218 |
| iterations | 56 |
| time_elapsed | 5528 |
| total_timesteps | 1209600 |
| train/ | |
| approx_kl | 5.842441 |
| clip_fraction | 0.37 |
| clip_range | 0.2 |
| entropy_loss | -0.437 |
| explained_variance | 0.733 |
| learning_rate | 0.00025 |
| loss | 20 |
| n_updates | 550 |
| policy_gradient_loss | 0.0759 |
| value_loss | 160 |
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| time/ | |
| fps | 218 |
| iterations | 57 |
| time_elapsed | 5629 |
| total_timesteps | 1231200 |
| train/ | |
| approx_kl | 6.230382 |
| clip_fraction | 0.367 |
| clip_range | 0.2 |
| entropy_loss | -0.358 |
| explained_variance | 0.769 |
| learning_rate | 0.00025 |
| loss | 11.6 |
| n_updates | 560 |
| policy_gradient_loss | 0.0837 |
| value_loss | 123 |
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| time/ | |
| fps | 218 |
| iterations | 58 |
| time_elapsed | 5730 |
| total_timesteps | 1252800 |
| train/ | |
| approx_kl | 7.5136166 |
| clip_fraction | 0.376 |
| clip_range | 0.2 |
| entropy_loss | -0.477 |
| explained_variance | 0.675 |
| learning_rate | 0.00025 |
| loss | 16.6 |
| n_updates | 570 |
| policy_gradient_loss | 0.596 |
| value_loss | 168 |
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| time/ | |
| fps | 218 |
| iterations | 59 |
| time_elapsed | 5830 |
| total_timesteps | 1274400 |
| train/ | |
| approx_kl | 4.328797 |
| clip_fraction | 0.319 |
| clip_range | 0.2 |
| entropy_loss | -0.506 |
| explained_variance | 0.714 |
| learning_rate | 0.00025 |
| loss | 3.97 |
| n_updates | 580 |
| policy_gradient_loss | 0.0452 |
| value_loss | 96.6 |
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| time/ | |
| fps | 218 |
| iterations | 60 |
| time_elapsed | 5932 |
| total_timesteps | 1296000 |
| train/ | |
| approx_kl | 8.380802 |
| clip_fraction | 0.388 |
| clip_range | 0.2 |
| entropy_loss | -0.29 |
| explained_variance | 0.524 |
| learning_rate | 0.00025 |
| loss | 33.6 |
| n_updates | 590 |
| policy_gradient_loss | 0.0855 |
| value_loss | 268 |
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| time/ | |
| fps | 218 |
| iterations | 61 |
| time_elapsed | 6034 |
| total_timesteps | 1317600 |
| train/ | |
| approx_kl | 7.3953514 |
| clip_fraction | 0.399 |
| clip_range | 0.2 |
| entropy_loss | -0.38 |
| explained_variance | 0.674 |
| learning_rate | 0.00025 |
| loss | 21.8 |
| n_updates | 600 |
| policy_gradient_loss | 0.0652 |
| value_loss | 142 |
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| time/ | |
| fps | 218 |
| iterations | 62 |
| time_elapsed | 6136 |
| total_timesteps | 1339200 |
| train/ | |
| approx_kl | 6.8781967 |
| clip_fraction | 0.446 |
| clip_range | 0.2 |
| entropy_loss | -0.481 |
| explained_variance | 0.668 |
| learning_rate | 0.00025 |
| loss | 12.2 |
| n_updates | 610 |
| policy_gradient_loss | 0.0566 |
| value_loss | 230 |
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| time/ | |
| fps | 218 |
| iterations | 63 |
| time_elapsed | 6238 |
| total_timesteps | 1360800 |
| train/ | |
| approx_kl | 15.005539 |
| clip_fraction | 0.503 |
| clip_range | 0.2 |
| entropy_loss | -0.357 |
| explained_variance | 0.601 |
| learning_rate | 0.00025 |
| loss | 11.9 |
| n_updates | 620 |
| policy_gradient_loss | 0.094 |
| value_loss | 290 |
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| time/ | |
| fps | 218 |
| iterations | 64 |
| time_elapsed | 6340 |
| total_timesteps | 1382400 |
| train/ | |
| approx_kl | 8.899053 |
| clip_fraction | 0.429 |
| clip_range | 0.2 |
| entropy_loss | -0.371 |
| explained_variance | 0.692 |
| learning_rate | 0.00025 |
| loss | 31.5 |
| n_updates | 630 |
| policy_gradient_loss | 0.066 |
| value_loss | 397 |
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| time/ | |
| fps | 217 |
| iterations | 65 |
| time_elapsed | 6443 |
| total_timesteps | 1404000 |
| train/ | |
| approx_kl | 7.4874077 |
| clip_fraction | 0.414 |
| clip_range | 0.2 |
| entropy_loss | -0.448 |
| explained_variance | 0.721 |
| learning_rate | 0.00025 |
| loss | 37.3 |
| n_updates | 640 |
| policy_gradient_loss | 0.0549 |
| value_loss | 340 |
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| time/ | |
| fps | 217 |
| iterations | 66 |
| time_elapsed | 6545 |
| total_timesteps | 1425600 |
| train/ | |
| approx_kl | 7.90197 |
| clip_fraction | 0.394 |
| clip_range | 0.2 |
| entropy_loss | -0.46 |
| explained_variance | 0.81 |
| learning_rate | 0.00025 |
| loss | 30.7 |
| n_updates | 650 |
| policy_gradient_loss | 0.0613 |
| value_loss | 386 |
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| time/ | |
| fps | 217 |
| iterations | 67 |
| time_elapsed | 6648 |
| total_timesteps | 1447200 |
| train/ | |
| approx_kl | 8.340442 |
| clip_fraction | 0.474 |
| clip_range | 0.2 |
| entropy_loss | -0.397 |
| explained_variance | 0.591 |
| learning_rate | 0.00025 |
| loss | 10.1 |
| n_updates | 660 |
| policy_gradient_loss | 0.0815 |
| value_loss | 332 |
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| time/ | |
| fps | 217 |
| iterations | 68 |
| time_elapsed | 6750 |
| total_timesteps | 1468800 |
| train/ | |
| approx_kl | 6.413869 |
| clip_fraction | 0.398 |
| clip_range | 0.2 |
| entropy_loss | -0.347 |
| explained_variance | 0.715 |
| learning_rate | 0.00025 |
| loss | 10.5 |
| n_updates | 670 |
| policy_gradient_loss | 0.0582 |
| value_loss | 187 |
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| time/ | |
| fps | 217 |
| iterations | 69 |
| time_elapsed | 6851 |
| total_timesteps | 1490400 |
| train/ | |
| approx_kl | 30.057222 |
| clip_fraction | 0.532 |
| clip_range | 0.2 |
| entropy_loss | -0.359 |
| explained_variance | 0.552 |
| learning_rate | 0.00025 |
| loss | 38.8 |
| n_updates | 680 |
| policy_gradient_loss | 0.112 |
| value_loss | 676 |
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| time/ | |
| fps | 217 |
| iterations | 70 |
| time_elapsed | 6952 |
| total_timesteps | 1512000 |
| train/ | |
| approx_kl | 13.428986 |
| clip_fraction | 0.376 |
| clip_range | 0.2 |
| entropy_loss | -0.345 |
| explained_variance | 0.663 |
| learning_rate | 0.00025 |
| loss | 104 |
| n_updates | 690 |
| policy_gradient_loss | 0.0895 |
| value_loss | 434 |
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| time/ | |
| fps | 217 |
| iterations | 71 |
| time_elapsed | 7051 |
| total_timesteps | 1533600 |
| train/ | |
| approx_kl | 16.452497 |
| clip_fraction | 0.383 |
| clip_range | 0.2 |
| entropy_loss | -0.355 |
| explained_variance | 0.618 |
| learning_rate | 0.00025 |
| loss | 33.7 |
| n_updates | 700 |
| policy_gradient_loss | 0.0797 |
| value_loss | 527 |
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| time/ | |
| fps | 217 |
| iterations | 72 |
| time_elapsed | 7152 |
| total_timesteps | 1555200 |
| train/ | |
| approx_kl | 6.3227 |
| clip_fraction | 0.338 |
| clip_range | 0.2 |
| entropy_loss | -0.424 |
| explained_variance | 0.795 |
| learning_rate | 0.00025 |
| loss | 18.5 |
| n_updates | 710 |
| policy_gradient_loss | 0.0543 |
| value_loss | 230 |
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| time/ | |
| fps | 217 |
| iterations | 73 |
| time_elapsed | 7252 |
| total_timesteps | 1576800 |
| train/ | |
| approx_kl | 9.170609 |
| clip_fraction | 0.442 |
| clip_range | 0.2 |
| entropy_loss | -0.412 |
| explained_variance | 0.711 |
| learning_rate | 0.00025 |
| loss | 53.1 |
| n_updates | 720 |
| policy_gradient_loss | 0.0672 |
| value_loss | 422 |
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| time/ | |
| fps | 217 |
| iterations | 74 |
| time_elapsed | 7354 |
| total_timesteps | 1598400 |
| train/ | |
| approx_kl | 4.309461 |
| clip_fraction | 0.332 |
| clip_range | 0.2 |
| entropy_loss | -0.402 |
| explained_variance | 0.826 |
| learning_rate | 0.00025 |
| loss | 1.44e+03 |
| n_updates | 730 |
| policy_gradient_loss | 0.0632 |
| value_loss | 239 |
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| time/ | |
| fps | 217 |
| iterations | 75 |
| time_elapsed | 7455 |
| total_timesteps | 1620000 |
| train/ | |
| approx_kl | 13.04697 |
| clip_fraction | 0.441 |
| clip_range | 0.2 |
| entropy_loss | -0.325 |
| explained_variance | 0.711 |
| learning_rate | 0.00025 |
| loss | 32.8 |
| n_updates | 740 |
| policy_gradient_loss | 0.0714 |
| value_loss | 356 |
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